Apparatus and methods for superimposing two-dimensional prints onto three-dimensional models of a part for manufacture

ABSTRACT

An apparatus for superimposing prints of a part for manufacture onto computer models of the part for manufacture is disclosed. The apparatus includes a processor and a memory communicatively connected to the processor. The memory containing instructions configuring the at least a processor to receive a computer model of the part for manufacture and a print of a part for manufacture. The processor decomposes a side view of the print, matches features of the part for manufacture in the side view to features of the part for manufacture in the computer model, superimposes a first plurality of object lines in the computing model onto a second plurality of object lines in the side view, and transfers the first semantic datum from the side view to the computer model.

FIELD OF THE INVENTION

The present invention generally relates to the field of computer-aideddesign and structure fabrication. In particular, the present inventionis directed to apparatus and methods for superimposing prints of a partfor manufacture onto computer models of the part for manufacture.

BACKGROUND

Computer-aided drawings typically convey information about ato-be-fabricated structure, such as a part or an assembly of componentsof a part. However, pertinent information in computer-aidedthree-dimensional models is sometimes missing.

SUMMARY OF THE DISCLOSURE

In an aspect of the disclosure is an apparatus for superimposing printsof a part for manufacture onto computer models of the part formanufacture, the apparatus including at least a processor; and a memorycommunicatively connected to the processor, the memory containinginstructions configuring the at least a processor to: receive a computermodel of the part for manufacture, the computer model comprising a firstplurality of object lines; receive a print of a part for manufacture,the print comprising a side view of the part for manufacture, the sideview comprising a first semantic datum and a second plurality of objectlines; decompose the side view, wherein decomposing the side viewcomprises identifying and distinguishing the side view from a remainderof the print; match features of the part for manufacture in the sideview to features of the part for manufacture in the computer model,wherein matching further comprises matching based on a distance betweena first object line of the first plurality of object lines and a firstobject line of the second plurality of object lines, wherein the firstobject line of the first plurality of object lines and the first objectline of the second plurality of object lines are object lines of a firstfeature of the features of the part for manufacture; superimpose thefirst plurality of object lines in the computing model onto the secondplurality of object lines in the side view; and transfer the firstsemantic datum from the side view to the computer model.

In another aspect of the disclosure is a method for superimposing printsof a part for manufacture onto computer models of the part formanufacture, the method including receiving, at a processor, a computermodel of the part for manufacture, the computer model comprising a firstplurality of object lines; receiving, at the processor, a print of apart for manufacture, the print comprising a side view of the part formanufacture, the side view comprising a first semantic datum and asecond plurality of object lines; decomposing, by the processor, theside view, wherein decomposing the side view comprises identifying anddistinguishing the side view from a remainder of the print; matching, bythe processor, features of the part for manufacture in the side view tofeatures of the part for manufacture in the computer model, whereinmatching further comprises matching based on a distance between a firstobject line of the first plurality of object lines and a first objectline of the second plurality of object lines, wherein the first objectline of the first plurality of object lines and the first object line ofthe second plurality of object lines are object lines of a first featureof the features of the part for manufacture; superimposing, by theprocessor, the first plurality of object lines in the computing modelonto the second plurality of object lines in the side view; andtransferring, by the processor, the first semantic datum from the sideview to the computer model.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a high-level block diagram illustrating an embodiment of anapparatus for superimposing prints of a part for manufacture ontocomputer models of the part for manufacture;

FIG. 2 illustrates a block diagram of an embodiment of amachine-learning module;

FIG. 3 is a block diagram illustrating an embodiment of an internaldatabase;

FIG. 4 is a screen shot of an exemplary embodiment of a superposition ofa side view and sematic datum on a computer model;

FIG. 5 is a process flow diagram illustrating an embodiment of a methodfor generating an instant design for manufacturability of a part at acomputing device; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatus and methods for superimposing prints of a part for manufactureonto computer models of the part for manufacture. Apparatus including atleast a processor and a memory communicatively connected to theprocessor. The memory contains instructions configuring the processor toperform the tasks described in this disclosure. The processor receives acomputer model of a part for manufacture and print of the part formanufacture. The computer model may include a three-dimensional image ofthe part for manufacture, and the print may include a two-dimensionalimage of the part for manufacture. The print may include one or moreside views of the part for manufacture and/or one or more section viewsof the part for manufacture. The print includes semantic datum. Theprocessor decomposes the side view, matches features of the part formanufacture from the side view to features of the part for manufacturefrom the computer model, superimposes object lines of the computingmodel of the part for manufacture onto object lines of the side view ofthe part for manufacture, and transfers semantic datum from the sideview to the computer model.

Each manufacturing process used may be any suitable process, such as,subtractive manufacturing, additive manufacturing, and formingmanufacturing. Examples of manufacturing processes that may be usedinclude, but are not limited to, rotary-tool milling, drilling, turning,electronic discharge machining, ablation, etching, erosion, cutting,cleaving, water jet, laser cut, 3D printing, injection molding, casting,stamping, forming, depositing, extruding, sintering, grinding, polishingamong others. Fundamentally, there is no limitation on the type ofmanufacturing process(es) used.

In some embodiments, the equipment used for manufacturing a part, suchas adding, removing, and/or forming material may be of the computerizednumerical control (CNC) type that is automated and operates by preciselyprogrammed commands that control movement of one or more parts of theequipment to effect the material. CNC machines, their operation,programming, and relation to computer aided manufacturing (CAM) toolsand computer aided design (CAD) tools are well known and need not bedescribed in detail herein for those skilled in the art to understandthe scope of the present invention and how to practice it in any of itswidely varying forms.

Referring now to FIG. 1 , an apparatus for superimposing 2D prints of apart for manufacture onto 3D computer models of the part for manufactureis illustrated. Apparatus 100 includes a processor 104. Processor 104may include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Processor 104 may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Processor104 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting processor 104 toone or more of a variety of networks, and one or more devices. Examplesof a network interface device include, but are not limited to, a networkinterface card (e.g., a mobile network interface card, a LAN card), amodem, and any combination thereof. Examples of a network include, butare not limited to, a wide area network (e.g., the Internet, anenterprise network), a local area network (e.g., a network associatedwith an office, a building, a campus or other relatively smallgeographic space), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Processor 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Processor 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Processor 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Processor 104 may be implemented usinga “shared nothing” architecture in which data is cached at the worker,in an embodiment, this may enable scalability of apparatus 100 and/orcomputing device.

With continued reference to FIG. 1 , processor 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, processor 104 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Apparatus 100 includes a memory 108 communicatively connected toprocessor 104. As used in this disclosure, “communicatively connected”means connected by way of a connection, attachment or linkage betweentwo or more relata which allows for reception and/or transmittance ofinformation therebetween. For example, and without limitation, thisconnection may be wired or wireless, direct or indirect, and between twoor more components, circuits, devices, systems, and the like, whichallows for reception and/or transmittance of data and/or signal(s)therebetween. Data and/or signals therebetween may include, withoutlimitation, electrical, electromagnetic, magnetic, video, audio, radioand microwave data and/or signals, combinations thereof, and the like,among others. A communicative connection may be achieved, for exampleand without limitation, through wired or wireless electronic, digital oranalog, communication, either directly or by way of one or moreintervening devices or components. Further, communicative connection mayinclude electrically coupling or connecting at least an output of onedevice, component, or circuit to at least an input of another device,component, or circuit. For example, and without limitation, via a bus orother facility for intercommunication between elements of a computingdevice. Communicative connecting may also include indirect connectionsvia, for example and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure.

Memory 108 may be configured to store information and/or datum relatedto apparatus 100, such as a print 132 of a part for manufacture and/or acomputer model of a part for manufacture as discussed below. In one ormore embodiments, memory 108 may be communicatively connected to aprocessor 104 and configured to contain instructions configuringprocessor 104 to execute any operations discussed in this disclosure. Inone or more embodiments, memory 108 may include a storage device, asdescribed further in this disclosure below.

Processor 104 is configured to receive a computer model 112 of a partfor manufacture. A “part for manufacture,” as used in this disclosure,is a part to be manufactured, wherein manufacturing may include anymanufacturing process as described in the entirety of this disclosure.The part may include any item made of materials such as metalsincluding, for example, aluminum and steel alloys, brass, and the like,plastics, such as nylon, acrylic, ABS, Delrin, polycarbonate, and thelike, foam, composites, wood, etc. A “computer model”, as described inthis disclosure, is a digital model of a physical structure, forinstance and without limitation as created using a three-dimensionalmodeling application such as but not limited to computer-aided design(CAD) modeling software. Computer model 112 may include athree-dimensional image of part for manufacture. As used in thisdisclosure, a “three-dimensional image” is an image having, appearing tohave, or displaying three dimensions, such as length, width, and height.For example and without limitation, computer-aided design (CAD) softwaremay include SOLIDWORKS® software and/or CATIA software (available fromDassault Systèmes SolidWorks Corp, Waltham, Mass.), AUTOCAD® softwareand/or Fusion 360 software (available from Autodesk, Inc., San Rafael,Calif.), PTC Creo software (available from PTC, Inc., Boston, Mass.),Siemens NX software (available from Siemens PLM Software, Plano, Tex.)and MICROSTATION® software (available from Bentley Systems, Inc., Exton,Pa.), and the like. Computer model 112 may further include any datadescribing and/or relating to a computer model of a part to bemanufactured. Computer model 112 may include any modeling type, such as,without limitation, a wireframe, solid model and/or any combinationthereof. Computer model 112 may be saved in a computer file using anysuitable file protocol, such as, without limitation, SolidWorks partfile (.SLDPRT), several SolidWorks part files organized into a singleassembly (.SLDASM), 3D assembly file supported by various mechanicaldesign programs (.STP), graphics file saved in a 3D vector format basedon the Initial Graphics Exchange Specification (.IGS) and/or the like.Computer model 112 may further include information about the geometryand/or other defining properties of the structure of part formanufacture. Computer model 112 may include a polygon mesh, such as acollection of vertices, edges, and faces, that define the shape ofcomputer model. For example and without limitation, the faces of thepolygon mesh may include triangles, such as a triangle mesh,quadrilaterals, or other simple convex polygons. Computer model 112 maybe created using 3D modeling software such as 3ds Max Design, Modo,polygonal modeling, spline modeling, digital sculpting, 3D scanning,and/or the like.

Still referring to FIG. 1 , computer model 112 may include a pluralityof sides of part for manufacture 116. Each side of the plurality ofsides, as used in this disclosure, may be a view of computer model 112from a plane orthogonal to an axis passing through an origin of computermodel 112. The axis may include, as a non-limiting example, a three-axiscoordinate system, such as the x-axis, y-axis, and z-axis, or abscissa,ordinate, and applicate. The axis may include, as a further non-limitingexample, any axis as a function of the origin. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofaxis which may be suitable for use as each side of the plurality ofsides consistently with this disclosure. The origin of the computermodel, as described herein, is a fixed point of reference for computermodel 112. For example and without limitation, the origin may includethe center of mass, the geometric center, the center of a feature of thepart, wherein a feature may be a hole, a bossa counter sink, a fillet, achamfer, a cylindrical surface, a groove, a pocket, a channel, extrudedvolume, revolved volume and the like. As a further example and withoutlimitation, the origin may include any position of computer model 112.

Continuing to refer to FIG. 1 , computer model 112 may include semanticinformation of part for manufacture 116. “Semantic information”, asdescribed in this disclosure, is data concerning and/or describingproduct and manufacturing information (PMI) and/or product life cyclemanagement (PLM). PMI, as used in this disclosure, is data describingnon-geometric attributes of a model of a part for manufacture, such ascomputer model 112, necessary for manufacturing the part, components ofthe part, and associated assemblies. For example and without limitation,PMI may include geometric dimensions and tolerances, 3D annotation anddimensions, surface finish, material specifications, and the like. PMImay include textual data, such as alphanumeric, punctuation,typographical symbols, character, string data, and/or any textual dataas described in the entirety of this disclosure. PLM, as used in thisdisclosure, is any data concerning and/or describing management of thelifecycle of the part from inception, through engineering design andmanufacture, to service and disposal of the manufactured part. PLM mayinclude textual data, such as alphanumeric, punctuation, typographicalsymbols, character, string data, and/or any textual data as described inthe entirety of this disclosure. In an embodiment, semantic informationincluded in computer model 112 may be used in processes for pricing apart to be manufactured.

With continued reference to FIG. 1 , processor 104 is configured toreceive a print 132 of part for manufacture 116. A “print”, as describedin this disclosure, is a digital model of a physical structure, forinstance and without limitation as created using a two-dimensionalmodeling application. Print 132 may be a file type that does not readilypermit extraction of semantic datum, such as a PDF, DXF, DWG, png,and/or jpg files. Print 132 may be any two-dimensional print 132 of partfor manufacture 116, such that the two-dimensional print 132 may includeany data describing the part for manufacture 116. As used in thisdisclosure, “two-dimensional” means having, appearing to have, ordisplaying two out of the three dimensions length, width, and height.Print 132, such as side view 136 and/or section view 140, may includesemantic datum. As used in this disclosure, “semantic datum” is anelement of data describing and/or identifying semantic information inand/or from a print of a part for manufacture. Semantic datum 128 mayinclude geometric dimensions and tolerances such as geometric tolerance,3D annotation and dimensions, surface roughness, surface finish,material specifications, center lines, break line, continuous line,hidden line, symmetry lines, leader lines, notes, PMI, PLM, and thelike. As used in this disclosure, a “geometric tolerance” is aquantified limit of allowable error of one or more physical attributesof a part for manufacture. Semantic datum 128 may include a formtolerance such as straightness, flatness, circularity, and/orcylindricity; a profile tolerance such as profile of a line and/orprofile of a surface; an orientation tolerance such as angularity,perpendicularity, and/or parallelism; location tolerance such asposition, concentricity and/or symmetry; a runout tolerance such ascircular runout and/or total runout; and the like. Semantic datum 128may be included in print 132 of part for manufacture 116 as symbols,annotations, numerical values, text, embedded information, and/or thelike. As used in this disclosure, “text” includes letters, numbers,and/or symbols. Print 132 may include an image representing part formanufacture 116 or a component of the part for manufacture 116, a numberrepresenting a numerical tolerance of the component, and/or an indicatorthat identifies the numerical tolerance is associated with thecomponent. Print 132 may also indicate a unit of measurement and/or ascale, which may be included in semantic datum 128 or on which semanticdatum 128 may be based. For example, print 132 may state that thedimensions are in inches, list the scale as “2:1”, include a circlerepresenting an exterior cylindrical surface of part for manufacture116, and have an arrow pointing from “R0.5000+/−0.0003” to the circle.Processor 104 may be configured to recognize “+/−” as a symbolrepresenting a tolerance for the preceding number in the amount of thesucceeding number. Processor 104 may also be configured to identify thearrow and that it is point from the numbers to the circle and determinedthat the tolerance is for the circle, specifically the radius of thecircle. Processor 104 may be configured to identify the unit ofmeasurement stated in print 132 and determine that the radius tolerancefor the circle is +/−0.0003 inches. Processor 104 may also be configuredto identify measurement scale and adjust numbers, including semanticdatum 128, accordingly. Processor 104 may be configured to determinescale by comparing an annotation of measurement to an actual length inprint 132. In some embodiments, processor 104 may recognize encoding ina file of print 132 as representing semantic datum 128 and may extractsemantic datum 128 from the file. Print 132 may include semanticinformation of part for manufacture 116 such as geometric dimensioningand tolerancing (GD&T) information, which may be provided in one or moresoftware files such as DXF files, DWG files, PDF files, png files, jpgfiles and/or the like.

Print 132 may be received with computer model 112 of part formanufacture 116 or received in a separate transmission and/or fromanother source. Processor 104 may receive computer model 112 and/orprint 132 from a user device 120. User device 120 may include, withoutlimitation, a transmission of communication with at least a server;transmission may include any transmission as described herein. A userdevice 120 may include an additional computing device, such as a mobiledevice, laptop, desktop computer, or the like; as a non-limitingexample, the user device 120 may be a computer and/or workstationoperated by an engineering professional. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousdevices which may be suitable for use as user device 120 consistentlywith this disclosure. Computer model 112 and/or print 132 may be storedin a database 124, which may be in apparatus 100 or remote. Database 124may store information pertaining to, for example, a request for a partto be manufactured, various machines used in manufacturing, materialsused to manufacture the part, and the like. Database 124 is described inmore detail below in reference to FIG. 3 . Print 132 and/or computermodel 112 may be retrieved from memory 108 and/or database 124.

Continuing to refer to FIG. 1 , database 124 may be implemented, withoutlimitation, as a relational database, a key-value retrieval datastoresuch as a NOSQL database, or any other format or structure for use as adatastore that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Dataset may be stored inany suitable data and/or data type. For instance and without limitation,dataset may include textual data, such as numerical, character, and/orstring data. Textual data may include a standardized name and/or codefor in-process and/or post-processing manufacturing, or the like; codesmay include raw material codes, dimensional codes, calibration codes,mechanical and/or thermal testing codes, safety codes, and/or dataformatting codes, which may include without limitation codes used in CAD3D geometry, assembly and PMI standards such as STEP AP242 and ASMEY14.5 geometric dimensioning and tolerancing (GD&T) symbols. In general,there is no limitation on forms textual data or non-textual data used asdataset may take; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms which may besuitable for use as dataset consistently with this disclosure.

Still referring to FIG. 1 , print 132 may include orthogonal side views136 of part for manufacture 116 such as a front view, top view, and/orleft or right view. As used in this disclosure, a “side view” is animage of a side of a part for manufacture 116. For example, side view136 may be a view of a right side or left side of part for manufacture116. Print 132 may also include one or more section views 140 of partfor manufacture 116. A “section view”, as used in this disclosure, is animage of a part for manufacture 116 that includes an internal section ofthe part for manufacture 116. An example of a section view 140 is across-sectional view of the part for manufacture 116. Processor 104 isconfigured to decompose side view 136 of print 132 of part formanufacture 116. Processor 104 may also be configured to decomposesection view 140 of print 132 of part for manufacture 116. As used inthis disclosure, to “decompose” is to locate and identify an object,feature, image, and/or component thereof in a print 132, such as a viewof a part for manufacture included in the print 132 or a feature of apart for manufacture 116 within a view in the print 132. A “feature”, asused in this disclosure, is a distinct component of part formanufacture. Examples of a feature include a detent, hole, indentation,pulley, edge, corner, curve, contour, side, circle, ellipse, line,cylindrical surface, plane, groove, ring, fillet, boss and/or the like.Decomposing side view 136 and/or section view 140 of print 132 mayinclude identifying and distinguishing the side view 136 and/or thesection view 140 from the remainder of print 132 such that processor 104may compare the side view 136 and/or the section view 140 in relation tocomputer model 112 separately from the remainder of the print 132.Decomposing side view 136 and/or section view 140 of print 132 mayinclude utilizing bounding boxes. A bounding box is a rectangularperimeter that surrounds an identified object, feature, image, and/orcomponent thereof in print 132, such as side view 136, section view 140,and/or feature within side view 136 or section view 140. Bounding boxesmay be used to locate and identify side view 136 and/or section view 140in print 132. Bounding boxes may be used to distinguish and separate therespective views from each other and any remaining portion of the print132. Bounding boxes may be used to locate and identify one or morefeatures of part for manufacture 116 in print 132, such as features inside view 136 and/or features in section view 140. Bounding box mayinclude the smallest rectangular dimension that fits the entireidentified object, feature, image, and/or component thereof within thebounding box. For example, if side view 136 of part for manufacture 116,as shown in print 132, is three inches tall as its greatest length andfour inches wide at its greatest width, bounding box encompassing theside view 136 may be a rectangle approximately three inches high andfour inches wide. As another example, if a feature of part formanufacture 116 shown in side view 136 as an ellipse is two inches talland one inch wide, a bounding box encompassing the feature may be arectangle approximately two inches high and one inch wide.

With continued reference to FIG. 1 , decomposing side view 136 of print132 and/or section view 140 of print 132 may utilize a machine-learningprocess. Processor 104 may be configured to implement algorithms orgenerate a machine-learning module, such as bounding module 144.Bounding module 144 may be consistent with machine-learning module 200,which is described in more detail below in reference to FIG. 2 .Bounding module 144 may be configured to decompose side view 136 and/orsection view 140 in print 132 using bounding boxes. Bounding module 144may also be configured to apply labels to bonding boxes, such as labelsto identify the type of view, such as “side view” and/or “section view”.Bounding module 144 may include a machine-learning model, which may betrained on the processor 104 or another device. A “machine-learningmodel,” as used in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory 108; aninput is submitted to a machine-learning model once created, whichgenerates an output based on the relationship that was derived. Trainingdata may correlate inputs of prints 132 and outputs and correspondingprints 132 with bounding boxes encompassing all side views 136 and/orall section views 140 in prints 132. Training data is a plurality ofdata entries containing a plurality of inputs that are correlated to aplurality of outputs for training a processor 104 by a machine-learningprocess to decompose side view 136 and/or section view 140. In someembodiments, outputs may further include labels applied to the boundingboxes in prints 132 corresponding to input prints 132. Training data maybe collected by recording original prints 132 and the correspondingprints 132 with bounding boxes and/or labels manually applied to allsides view 150 and/or all section views 140 in the prints 132. Trainingdata may be stored in database 124, and processor 104 may becommunicatively connected to database 124. Training data may becollected from journal articles, publicly available information, manualextractions of semantic data from prints 132, iterations of amachine-learning process such as outputs of previous iterations ofbounding model 144, self-learning, user inputs, previous iterations ofmethods described in this disclosure, and the like. Training data mayinclude DXF, DWG, PDF, jpg, and/or png files. Processor 104 may utilizemachine-learning process and output bounding boxes and/or labelsdecomposing side view 136 and/or section view 140 based on one or moreinputs discussed above and training data.

Bounding module 144 may use a classifier. A “classifier,” as used inthis disclosure is a machine-learning model, such as a mathematicalmodel, neural net, or program generated by a machine learning algorithmknown as a “classification algorithm,” as described in further detailbelow, that sorts inputs into categories or bins of data, outputting thecategories or bins of data and/or labels associated therewith. Aclassifier may be configured to output at least a datum that labels orotherwise identifies a set of data that are clustered together, found tobe close under a distance metric as described below, or the like.Processor 104 and/or another device may generate a classifier using aclassification algorithm, defined as a process whereby a processor 104derives a classifier from training data. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. Classifier may classify and/or label views inprint 132 as side view 136 and/or section view 140.

Processor 104 may be configured to match features of part formanufacture 116 from side view 136 and/or section view 140 to featuresof part for manufacture 116 from computer model 112. Print 132, such asside view 136 and/or section 140, and computer model 112 may each have aplurality of object lines. In this disclosure, object lines in computermodel 112 may be called a “first plurality of object lines” and objectlines in print 132 may be called a “second plurality of object lines”.An object line may be a line in print 132 representing an outline of aplanar surface of part for manufacture 116. In side view 136, sectionview 140, and/or computer model 112, location of center of part formanufacture 116, location of object line of part for manufacture 116,and/or a distance between center of part for manufacture 116 and objectline of part for manufacture 116 may be determined by semantic datum inprint 132 and/or semantic datum in computer model 112 such as, forexample, annotation lines, centering marks, true position symbols,center lines, and the like. Processor 104 may utilize brute force bymatching features of part for manufacture 116 in side view 136 tofeatures of part for manufacture 116 in computer model 112 and filteringout matches based on corresponding distances between centers of part formanufacture 116 and object lines of part for manufacture 116 untilprocessor 104 determines a match wherein the corresponding distances areequal. Matching features in side view 136 and/or section view 140 tofeatures in computer model 112 may be based on a distance between acenter of a first feature of features of part for manufacture 116 incomputer model 112 and a center of the first feature of features of thepart for manufacture 116 in side view 136. Matching features in sideview 136 and/or section view 140 to features in computer model 112 mayalso be based on a distance between a first object line of firstplurality of object lines and a first object line of second plurality ofobject lines, wherein the first object line of the first plurality ofobject lines and the first object line of the second plurality of objectlines are object lines of the first feature. Processor 104 may beconfigured to determine a distance between first object line in computermodel 112 and first object line in side view 136, which may be, forexample, a Hausdorff distance between the corresponding object lines.For example, feature of part for manufacture 116 in side view 136, suchas a circle, may be matched and oriented in relation to correspondingfeature of part for manufacture 116 in computer model 112, such as acylinder, such that a Hausdorff distance between object lines of thefeature in the computer model 112 and object lines of the correspondingfeature in side view 136 is a minimum distance. Similarly, features incomputer model 112 may be matched to features in side view 136 such thata distance between a center of the feature in computer model 112 and acenter of the feature in side view 136 is a minimum distance. As used inthis disclosure, an “object line” is a line in a print and/or a computermodel used to outline a visible edge or contour of the part formanufacture or a broken line in a print and/or a computer model used tooutline an invisible edge and/or invisible contour of the part formanufacture. Processor 104 may be configured to match features of partfor manufacture 116 in a plurality of side views 136 to correspondingfeatures of the part for manufacture 116 in computer model 112. Matchingmay be based on an orientation of features in print 132, such as in sideview 136 and/or in section view 140, and an orientation of thecorresponding features in computer model 112. For example, matching maybe based on axis of features such as axis of cylinder, cone, boss,fillets and/or dimensions of features such as a radius of features likecircles, cylinders, boss, and fillets; a width of grooves; and/or aheight of boss in print 132 and dimensions of the corresponding featuresin computer model 112.

With continued reference to FIG. 1 , processor 104 may be configured toimplement algorithms or generate a machine-learning module, such as linemodule 148, to identify and/or classify lines in print 132. For example,line module 148 may be configured to distinguish object lines in print132 from other lines such as center lines, leader lines, dimensionlines, intersection lines, projection lines, and/or the like. Linemodule 148 may be consistent with machine-learning module 200, which isdescribed in more detail below in reference to FIG. 2 . Line module 148may include a machine-learning model, which may be trained on theprocessor 104 or another device. Training data is a plurality of dataentries containing a plurality of inputs that are correlated to aplurality of outputs for training a processor 104 by a machine-learningprocess. In some embodiments, outputs may further include labels appliedto the bounding boxes in prints 132 corresponding to input prints 132.Inputs of training data may include prints 132 and outputs of trainingdata may include the input prints 132 wherein all object lines in theprints 132 are identified. Training data may be collected by recordingoriginal prints 132 and the corresponding prints 132 with all objectlines identified in the prints 132. Training data may include DXF filesand/or DWG files of prints 132 of part for manufacture 116 withidentified object lines and PDF files, jpg files, and/or png files ofprints 132 of the same part for manufacture 116 with object lines thatare not identified or distinguished from other lines. Line module 144may use DXF files and/or DWG files of prints 132 to train to identifyobject lines in PDF files, jpg files, and/or png files of prints 132.Training data may be stored in database 124, and processor 104 may becommunicatively connected to database 124. Training data may becollected from journal articles, publicly available information, manualextractions of semantic data from prints 132, iterations of amachine-learning process such as outputs of previous iterations of linemodel 148, self-learning, user inputs, previous iterations of methodsdescribed in this disclosure, and the like. Processor 104 may utilizemachine-learning process and output bounding boxes and/or labelsdecomposing side view 136 and/or section view 140 based on one or moreinputs discussed above and training data. Line module 148 may use aclassifier, such as classifiers described in this disclosure, toclassify object lines. Classifier may classify and/or label lines,including object lines, in print 132.

Processor 104 is configured to superimpose object lines in computermodel 112 onto object lines in print 132, such as in side view 136and/or in section view 140. Superimposing may be based on a match offeatures of part for manufacture 116 in side view 136 to features ofpart for manufacture 116 in computer model 112 that processor 104matches as discussed above. Processor 104 may analyze whethersuperimposing is mismatched by determining whether object lines ofcomputer model 112 perfectly overlap object lines of print 132. If thecorresponding object lines are mismatched, processor 104 may optimizesuperimposing based on an object line of the side view 136 overlappingan object line of the computer model 112. Apparatus 100 may beconfigured to superimpose, as described in this disclosure, a pluralityof side views 136 of part for manufacture 116 onto computer model 112 ofthe part for manufacture 116.

Still referring to FIG. 1 , apparatus 100 may include graphicsprocessing unit (GPU) operating on processor 104. A “GPU”, as used inthis disclosure, is a device with a set of specific hardwarecapabilities that are intended to map well to the way that various 3Dengines execute their code, including geometry setup and execution,texture mapping, memory access, and/or shaders. GPU may be a processor,wherein a processor may include any processor as described in theentirety of this disclosure. GPU may include, without limitation, aspecialized electronic circuit designed to rapidly manipulate and altermemory 108 to accelerate the creation of images in a frame buffer. Forinstance, and without limitation, GPU may include a computer chip thatperforms rapid mathematical calculations, primarily for the purpose ofrendering images. GPU may further include, without limitation, fullscene anti-aliasing (FSAA) to smooth the edges of 3-D objects andanisotropic filtering (AF) to make images look crisper. GPU may include,without limitation, dedicated graphics cards, integrated graphics cards,hybrid graphics cards, and/or any combination thereof. GPU may beconfigured to optimize the superimposing of object lines of computingmodel of part for manufacture 116 onto object lines of print 132 of thepart for manufacture 116. For example, processor 104 may take theoverlapping of object lines of computer model 112 of part formanufacture 116 onto object lines of print 132 of the part formanufacture 116 as the objective function to be maximized over a set offeasible alternatives, namely resizing the object lines of the print132. Thus, processor 104 may determine a resizing of the secondplurality of object lines of print 132 that maximizes overlapping of thefirst plurality of object lines of computer model 112 over the secondplurality of object lines of the print 132. Based on whether resizing ofobject lines of print 132 is required and the specifications of anynecessary resizing, processor 104 may determine a scale factor betweenside view 136 and/or section view 140 and computer model 112. Processor104 may determine a coordinate transformation from print 132 to computermodel 112 based on the superimposing of object lines of computing modelof part for manufacture 116 onto object lines of print 132 of the partfor manufacture 116. If optimization is required, coordinatetransformation from print 132 to computer model 112 may also be based onthe resizing of object lines of the print 132.

Still referring to FIG. 1 , processor 104 may be configured to transfersemantic datum from the print 132, such as side view 136 and/or sectionview 140, to the computer model 112, which may be based on coordinatetransformation determined by the processor 104, thus resulting in thesemantic datum aligning with components and/or features in computermodel 112 that correspond to components and/or features in print 132. Asused in this disclosure, a “component” of a part for manufacture is afeature, part, and/or piece of the part for manufacture. Processor 104may be configured to superimpose object lines in computer model 112 ontocorresponding object lines in a plurality of side views 136.

With continued reference to FIG. 1 , processor 104 is configured to mapsemantic datum 128 on computer model 112 of part for manufacture 116.Semantic datum 128 may include a plurality of semantic data. Semanticdatum 128 may come from a plurality of side views 136. Mapping mayinclude inserting in computer model 112 semantic datum 128 from print132. Mapping may include positioning semantic datum 128 in computermodel 112 such that the semantic datum 128 aligns with the component ofpart for manufacture 116 in the computer 108 with which the semanticdatum 128 concerns. As used in this disclosure, a “component” of a partfor manufacture is a feature, part, and/or piece of the part formanufacture. For example, mapping may include inserting a semantic datum128 of a hole radius with an arrow that was pointing to the hole in theimage of the part for manufacture 116 in print 132 into computer model112 such that the arrow points to the corresponding hole in the image ofthe part for manufacture 116 in the computer model 112. Processor 104may be configured to compare dimensions and/or coordinates of componentsof part for manufacture 116 in print 132 and computer model 112 andassociate the same components to transfer semantic datum 128 extractedfrom the print 132 onto the computer model 112. Association may includematching one or more measurements and/or descriptions of a component inprint 132 with a component in computer model 112 including, withoutlimitation, coordinates, height, length, width, radius, position on partfor manufacture 116, and/or the like. For example, processor 104 mayassociate a circle in print 132 to a cylinder in computer model 112 bycomparing their corresponding radii, coordinates, and/or othermeasurements. As another example, processor 104 may associate a circledesignated by a position GD&T symbol assigned to a 0.5 diameter in print132 to the only cylinder with a 0.25 radius in computer model 112. Print132 may include a concentricity GD&T symbol assigned to a 0.75 inch holein print 132, which processor 104 may associate with the only 0.375 inchradius hole in computer model 112. Once components of part formanufacture 116 in print 132 are associated with their correspondingcomponents of the part for manufacture 116 in computer model 112, thenprocessor 104 may map semantic datum 128 on the computer model 112 whilemaintaining their relation to the corresponding measurements of thecomponents. Similarly, processor 104 may associate a line in print 132with a surface of part for manufacture 116 in computer model 112 by, forexample, comparing the length of the line with the length of the surfaceand/or comparing the positions of the line and surface on thecorresponding images of part for manufacture 116 and/or in relationalposition to other components of the part for manufacture 116. As anadditional example, mapping semantic datum 128 on computer model 112 ofthe part for manufacture 116 may further comprise associating a line ofthe print 132 of the part for manufacture 116 to a plane of the computermodel 112 of the part for manufacture 116. In some embodiments,processor 104 mapping semantic datum 128 on computer model 112 mayinclude superimposing computer model 112, which may be athree-dimensional image of part for manufacture 116, onto print 132,which may include a two-dimensional image of the part for manufacture116 and the original semantic datum 128, such that the three-dimensionalimage is rotated and positioned on the two-dimensional image. As used inthis disclosure, “superimposing” is placing or laying an image orinformation over another image or information. For example,superimposing a three-dimensional image of part for manufacture 116 ontoprint 132 may include rotating, aligning, and placing or laying thethree-dimensional image of the part for manufacture 116 onto the imageof the part for manufacture 116 in the print 132. Thus, the semanticdatum 128 on print 132 may align with the corresponding components ofcomputer model 112 of part for manufacture 116. In some embodiments,processor 104 may align an outer profile of a three-dimensional image ofpart for manufacture 116 in computer model 112 with an outer profile ofa two-dimensional image of part for manufacture 116 in print 132.

Still referring to FIG. 1 , processor 104 may be configured to importcomputer model 112, print 132, and/or information therein such assemantic datum 128 from a first at least a file to a second at least afile, wherein the second at least a file may include a format or filetype distinct from the first at least a file. Semantic datum 128 may beimported as a block or unit with a unique tag, which may identify thetype and/or origin of semantic datum 128 imported. Processor 104 may beconfigured to associate semantic datum 128 to corresponding componentsand/or measurements of part for manufacture 116 based on the tag.Processor 104 may be configured to recognize different types of lines,shapes, symbols including without limitation GD&T symbols, annotations,embedded text, and the like and extract information from therecognition. In some embodiments, the recognition is based onidentifying standardized formatting. For example, processor 104 may beconfigured to recognize a line by a unique symbol in print 132 as acenter line type. Processor 104 may then extract that the location ofthe line is the center of part for manufacture 116. As another example,processor 104 may be configured to identify and recognize that dimensionlines, leader lines, cutting planes, and projection lines are not thepart for manufacture 116. Processor 104 may recognize, for example, thatthe dimension lines, leader lines, cutting planes, and projection linesare not part for manufacture 116. Processor 104 may identify dimensionand tolerances by a type of line segment and associate the dimension andtolerances with the line segment. For example, processor 104 mayrecognize extension lines and a corresponding dimension line between theextension lines in print 132 as measuring a dimension of part formanufacture 116.

Continuing to refer to FIG. 1 , apparatus 100 may include an assignmentmodule operating on GPU. Assignment module may include any hardwareand/or software module. Assignment module may be configured to determineat least an orientation of computer model 112. Orientation, as describedherein, is a plane parallel to the direction of machining the part,wherein the plane may be positioned on any direction. The direction, asdescribed herein, may include any axis as described in the entirety ofthis disclosure. The axis may include, as a non-limiting example, athree-axis coordinate system, such as the x-axis, y-axis, and z-axis, orabscissa, ordinate, and applicate. As a further non-limiting example,the axis may include a five-axis system, such as two rotation axis,x-axis, y-axis, and z-axis. The axis may include, as a furthernon-limiting example, any rotational axis as a function of the origin.In order to machine the entirety of the part, the at least anorientation needs to include planes to ensure all features of computermodel 112 are machined wherein features include any feature as describedin the entirety of this disclosure. For example and without limitation,a cylindrical part may be machined in its entirety from the at least anorientation consisting of planes perpendicular to the rotational axis ofthe cylinder. As a further non-limiting example, a cylindrical part witha hole in one side may be machined in its entirety from the at least anorientation consisting on planes on perpendicular to the rotational axisof the cylinder and the radial axis. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousplanes which may be suitable for use the at least an orientationconsistently with this disclosure.

Continuing to refer to FIG. 1 , determining each orientation of theplurality of orientations may further include generating a geodesicrepresentative part model, wherein the geodesic representative partmodel includes the representative part model encased in a geodesicpolygon. The “geodesic sphere”, as described herein, is a computer modelof a sphere, wherein the sphere is comprised of triangular elements. Forexample and without limitation, the triangular elements of the geodesicsphere may include any frequency of triangles. Further the triangularelements may be arranged in orientation, as a non-limiting example, theorientations may include a platonic solid, such as a tetrahedron,hexahedron, octahedron, dodecahedron, and an icosahedron. Therepresentative part model may be encased in the geodesic sphere formingthe geodesic representative part model. The representative part modelmay be oriented inside the geodesic sphere such that the entirety of therepresentative part model is within the geodesic sphere. As an exampleand without limitation, the representative part model may be orientedsuch that the origin of the representative part model is aligned withthe origin of the geodesic sphere. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousmodels which may be suitable for use the geodesic representative partmodel consistently with this disclosure.

With continued reference to FIG. 1 , determining each orientation of theplurality of orientations may further include computing a thicknessdirection datum of the geodesic representative part model. The“thickness direction datum”, as described herein, is the minimumdistance between two parallel planes which enclose the representativepart model within the geodesic representative part model in a givendirection. Further, determining each orientation of the plurality oforientations may further include computing an accessibility conerepresented by the geodesic sphere. The “accessibility cone”, asdescribed herein, is the tool reachable orientation from the location ofthe part. The geodesic sphere may include, for example and withoutlimitation, any geodesic sphere as described in the entirety of thisdisclosure. For example and without limitation, the direction mayinclude any axis as described in the entirety of this disclosure, suchas an x-axis, y-axis, z-axis, and/or any rotational axis. As an exampleand without limitation, in 3-axis milling the z-axis may be assigned tocompute the thickness direction datum. As a further example and withoutlimitation, in 5-axis milling a rotational axis may be assigned to thedirection in the accessibility cone. In an embodiment and withoutlimitation, in 5-axis milling any axis may be assigned to the directionin the accessibility cone. In an embodiment, computing the accessibilitycone includes performing ray tracing, such as reverse order ray tracing,wherein rays of light are traced from the surface of the part in thedirection of the geodesic sphere. In the embodiment, ray tracing furtherincludes determining visibility directions, wherein the visibilitydirection is a ray of light that does not contact the surface of thepart. Ray tracing may include any methodology of ray tracing asdescribed herein. Further, in the embodiment, ray tracing furtherincludes conducting a tool reachability test on the GPU as a function ofthe visibility directions. For example and without limitation, the toolreachability test may include a collision test, gouge test, and thelike. In an embodiment and without limitation, the visibility cone maybe narrowed by taking into account the tool holder collision against theworkpiece. In the embodiment, the milling orientation may be configuredto be selected from the visibility cone. The visibility cone may includeany visibility information as described in the entirety of thisdisclosure. In an embodiment and without limitation, any visibilityinformation as described in the entirety of this disclosure, can bestored in the geodesic sphere and/or in the rasterized depth image ofthe representative part. In an embodiment, determining each orientationof the plurality of orientations may include computing a bitonic sortingalgorithm. The bitonic sorting algorithm, as described herein, is aparallel sorting algorithm that performs (log²(n)) comparisons. Forexample and without limitation, the bitonic sorting algorithm utilizesthe thickness direction datum and a dominant surface normal. Thedominant surface normal, as described herein, is the largest surfacenormal direction of each direction of the part. As an example andwithout limitation, the dominant surface normal is the z-axis when thez-height increase remains under 2× and the surface area is larger thanthe 20% of the XY plane projected area of the representative part model.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various distances which may be suitable foruse as the thickness direction datum consistently with this disclosure.

Still referring to FIG. 1 , dataset may be stores as image data, such asfor example an image of a particular CNC mechanical part, such as acomputer model of a threaded bolt, a computer-aided design of astainless-steel endcap, or a tool path of a hollow box. Image data maybe stored in various forms including for example, joint photographicexperts group (JPEG), exchangeable image file format (Exif), taggedimage file format (TIFF), graphics interchange format (GIF), portablenetwork graphics (PNG), netpbm format, portable bitmap (PBM), portableany map (PNM), high efficiency image file format (HEIF), still pictureinterchange file format (SPIFF), better portable graphics (BPG), drawnfiled, enhanced compression wavelet (ECW), flexible image transportsystem (FITS), free lossless image format (FLIF), graphics environmentmanage (GEM), portable arbitrary map (PAM), personal computer exchange(PCX), progressive graphics file (PGF), gerber formats, 2 dimensionalvector formats, 3 dimensional vector formats, compound formats includingboth pixel and vector data such as encapsulated postscript (EPS),portable document format (PDF), SolidWorks part file (.SLDPRT), severalSolidWorks part files organized into a single assembly (.SLDASM), 3Dassembly file supported by various mechanical design programs (.STP),graphics file saved in a 2D/3D vector format based on the InitialGraphics Exchange Specification (.IGS) and stereo formats. Apparatus 100may be configured to display any resulting images, prints 132, and/orcomputer models 112 described in this disclosure to user device 120.Displaying may include any means of displaying as described in theentirety of this disclosure. Displaying to user device 120 may furthercomprise verifying, by the user at user device 120. Verifying mayinclude, for example and without limitation, any means of confirmation,such as viewing the resulting images, prints 132, and/or computer models112 and selecting a button. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various confirmationswhich may be suitable for use as verifying consistently with thisdisclosure.

Referring now to FIG. 2 , an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module 200may perform determinations of tolerance symbols, classification of linetypes, identify leader lines and extract annotations, purse GD&Tsymbols, verify specifications in semantic datum 128, identify ambiguousannotations and clarify the annotations, identify mismatches betweencomputer models 112 and prints 132 and correct the mismatches, guide theuser to clarify identified ambiguities in sematic information.Machine-learning module 200 may classify between number and letters andbetween symbols and lines, infer feasibility of the tolerance frommanufacturing data, infer the process and the cost to accomplish thetolerances specified by the semantics data and/or analysis steps,methods, processes, or the like as described in this disclosure usingmachine learning processes. A “machine learning process,” as used inthis disclosure, is a process that automatedly uses training data 204 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 208 given data provided as inputs 212;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 2 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 204 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 204 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 204 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 204 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 204 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 204 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data204 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2 ,training data 204 may include one or more elements that are notcategorized; that is, training data 204 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 204 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 204 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 204 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 2 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 216. Training data classifier 216 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 204. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 2 , machine-learning module 200 may beconfigured to perform a lazy-learning process 220 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 204. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 204 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 2 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 224. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 224 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 224 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 204set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 2 , machine-learning algorithms may include atleast a supervised machine-learning process 228. At least a supervisedmachine-learning process 228, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs described in this disclosure as inputs, outputs describedin this disclosure as outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 204. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 228 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 2 , machine learning processes may include atleast an unsupervised machine-learning processes 232. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2 , machine-learning module 200 may be designedand configured to create a machine-learning model 224 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 3 , an embodiment of database 124 is illustrated.Database 124 may be implemented as a hardware and/or software module.Database 124 may be implemented, without limitation, as a relationaldatabase, a key-value retrieval datastore such as a NOSQL database, orany other format or structure for use as a datastore that a personskilled in the art would recognize as suitable upon review of theentirety of this disclosure. Database 124 may contain datasets that maybe utilized by unsupervised machine-learning model 200 to find trends,cohorts, and shared datasets between data contained within database 124and computer model 112. In an embodiment, datasets contained withindatabase 124 may be categorized and/or organized according to sharedcharacteristics. For instance and without limitation, one or more tablescontained within database 124 may include representative part model datatable 300, wherein computer model data table 304 may include storedcomputer model 112. As a further example and without limitation, one ormore tables contained within database 124 may include print data table308, wherein print data table 308 may include stored print 132.

With continued reference to FIG. 3 , one or more tables contained withindatabase 124 may include semantic data table 312. Semantic data table312 may include shapes, symbols, annotations, text, embeddedinformation, and/or the like that contain semantic information asdiscussed above including the associated measurements and components ofpart for manufacture 116, and corresponding semantic datum 128.

Still referring to FIG. 3 , one or more tables contained within database124 may include bounding box data table 316. Bounding box data table 316may include training data such as prints 132 and the correspondingprints 132 with bounding boxes and/or labels applied to side view 136and/or section view 140.

Referring now to FIG. 4 , an embodiment of a superposition of side view136 and sematic datum 128 on a computer model 112 is illustrated.Computer model 400 includes a three-dimensional image of part formanufacture 116. Side view 136 and semantic datum 128 is mapped oncomputer model 400, as discussed in detail above pertaining to FIG. 1 .

Referring now to FIG. 5 , an embodiment of method 500 for superimposing2D prints of part for manufacture onto 3D computer models of part formanufacture is illustrated. At step 505, processor receives computermodel of part for manufacture, the computer model comprising a firstplurality of object lines; this may be implemented, without limitation,as described above in reference to FIGS. 1-5 . Processor may receivecomputer model from user device. Computer model may include a pluralityof sides. Computer model may comprise a three-dimensional geometry ofthe part for manufacture. Computer model may be received by processorutilizing any of the network methodology as described herein. Computermodel may include any computer model as described herein. Each side ofthe plurality of sides, as described herein, may be the plane of eachcoordinate in axis passing through the origin of the representative partmodel. For example and without limitation, the axis may include athree-axis coordinate system, such as the x-axis, y-axis, and z-axis, orabscissa, ordinate, and applicate. The axis may include, as a furthernon-limiting example, any rotational axis as a function of the origin,as described in further detail above in reference to FIG. 1 . In anembodiment, computer model 112 may further include semantic information.Semantic information may include any semantic information as describedabove in further detail in reference to FIG. 1 .

Still referring to FIG. 5 , at step 510, processor receives print ofpart for manufacture the print comprising a side view of the part formanufacture, the side view comprising a first semantic datum and asecond plurality of object lines; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-5 . Print mayinclude a two-dimensional image of part for manufacture. Semantic datumfrom print may include text. Print may include one or more side viewsand/or section views of part for manufacture.

With continued reference to FIG. 5 , at step 515, processor decomposesside view, wherein decomposing the side view comprises identifying anddistinguishing the side view from a remainder of print; this may beimplemented, without limitation, as described above in reference toFIGS. 1-5 . Extracting semantic datum may utilize a machine-learningprocess. Decomposing side view may comprise utilizing bounding boxes.Decomposing side view may utilize machine-learning process. Processormay decompose section view of part for manufacture.

Still referring to FIG. 5 , at step 520, processor matches features ofpart for manufacture in side view to features of part for manufacture incomputer model, wherein matching further comprises matching based on adistance between a center of a first feature of the features of the partfor manufacture in the computer model and a center of the first featureof the features of the part for manufacture in the side view, and adistance between a first object line of the first plurality of objectlines and a first object line of the second plurality of object lines,wherein the first object line of the first plurality of object lines andthe first object line of the second plurality of object lines are objectlines of the first feature; this may be implemented, without limitation,as described above in reference to FIGS. 1-5 . Processor may determine acoordinate transformation from the print to the computer model.Processor may determine scale factor between side view and computermodel.

With continued reference to FIG. 5 , at step 525, processor superimposesfirst plurality of object lines in computing model onto second pluralityof object lines in side view; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-5 . Processor mayoptimize superimposing by determining a rotation and resizing of thesecond plurality of object lines that maximizes overlapping of the firstplurality of object lines over the second plurality of object lines; andresizing the second plurality of object lines based on the object lineof the side view overlapping an object line of the computer modeldetermination.

With continued reference to FIG. 5 , at step 530, processor transfersfirst semantic datum from side view to computer model; this may beimplemented, without limitation, as described above in reference toFIGS. 1-5 . Transferring first semantic datum may further comprisetransferring the first semantic datum as a function of coordinatetransformation. Processor may transfer second semantic datum fromsection view to computer model.

Referring now to FIG. 5 , an embodiment of semantic datum mapped on acomputer model is illustrated. Computer model 500 includes athree-dimensional image of part for manufacture 116. Semantic datum 128is mapped on computer model 500, as discussed in detail above pertainingto FIG. 1 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods andsystems according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for superimposing two-dimensional(2D) prints of a part for manufacture onto three-dimensional (3D)computer models of the part for manufacture, the apparatus comprising:at least a processor; and a memory communicatively connected to theprocessor, the memory containing instructions configuring the at least aprocessor to: receive a computer model of the part for manufacture, thecomputer model comprising a first plurality of object lines; receive aprint of a part for manufacture, the print comprising a side view of thepart for manufacture, the side view comprising a first semantic datumand a second plurality of object lines; decompose the side view, whereindecomposing the side view comprises identifying and distinguishing theside view from a remainder of the print; match features of the part formanufacture in the side view to features of the part for manufacture inthe computer model, wherein matching further comprises matching basedon: a distance between a first object line of the first plurality ofobject lines and a first object line of the second plurality of objectlines, wherein the first object line of the first plurality of objectlines and the first object line of the second plurality of object linesare object lines of a first feature of the features of the part formanufacture; superimpose the first plurality of object lines in thecomputing model onto the second plurality of object lines in the sideview; and transfer the first semantic datum from the side view to thecomputer model.
 2. The apparatus of claim 1, wherein the at least aprocessor is configured to optimize superimposing the first plurality ofobject lines onto the second plurality of object lines by: determining aresizing of the second plurality of object lines that maximizesoverlapping of the first plurality of object lines over the secondplurality of object lines; and resizing the second plurality of objectlines based on the determination.
 3. The apparatus of claim 1, whereinthe at least a processor is configured to determine a coordinatetransformation from the print to the computer model.
 4. The apparatus ofclaim 3, wherein transferring the first semantic datum further comprisestransferring the first semantic datum as a function of the coordinatetransformation.
 5. The apparatus of claim 1, wherein the at least aprocessor is configured to determine a scale factor between the sideview and the computer model.
 6. The apparatus of claim 1, whereindecomposing the side view comprises decomposing the side view utilizingbounding boxes.
 7. The apparatus of claim 6, wherein decomposing theside view further comprises decomposing the side view utilizing amachine-learning process.
 8. The apparatus of claim 1, wherein: theprint comprises a section view of the part for manufacture.
 9. Theapparatus of claim 8, wherein the at least a processor is configured tosuperimpose the first plurality of object lines of the computing modelof the part for manufacture onto the second plurality of object lines ofthe section view of the part for manufacture.
 10. The apparatus of claim9, wherein the at least a processor is configured to transfer a secondsemantic datum from the section view to the computer model.
 11. A methodfor superimposing two-dimensional (2D) prints of a part for manufactureonto three-dimensional (3D) computer models of the part for manufacture,the method comprising: receiving, at a processor, a computer model ofthe part for manufacture, the computer model comprising a firstplurality of object lines; receiving, at the processor, a print of apart for manufacture, the print comprising a side view of the part formanufacture, the side view comprising a first semantic datum and asecond plurality of object lines; decomposing, by the processor, theside view, wherein decomposing the side view comprises identifying anddistinguishing the side view from a remainder of the print; matching, bythe processor, features of the part for manufacture in the side view tofeatures of the part for manufacture in the computer model, whereinmatching further comprises matching based on: a distance between a firstobject line of the first plurality of object lines and a first objectline of the second plurality of object lines, wherein the first objectline of the first plurality of object lines and the first object line ofthe second plurality of object lines are object lines of a first featureof the features of the part for manufacture; superimposing, by theprocessor, the first plurality of object lines in the computing modelonto the second plurality of object lines in the side view; andtransferring, by the processor, the first semantic datum from the sideview to the computer model.
 12. The method of claim 11, wherein the atleast a processor is configured to optimize superimposing the firstplurality of object lines onto the second plurality of object lines by:determining a resizing of the second plurality of object lines thatmaximizes overlapping of the first plurality of object lines over thesecond plurality of object lines; and resizing the second plurality ofobject lines based on the determination.
 13. The method of claim 11,wherein the at least a processor is configured to determine a coordinatetransformation from the print to the computer model.
 14. The method ofclaim 13, wherein transferring the first semantic datum furthercomprises transferring the first semantic datum as a function of thecoordinate transformation.
 15. The method of claim 11, wherein the atleast a processor is configured to determine a scale factor between theside view and the computer model.
 16. The method of claim 11, whereindecomposing the side view comprises decomposing the side view utilizingbounding boxes.
 17. The method of claim 16, wherein decomposing the sideview further comprises decomposing the side view utilizing amachine-learning process.
 18. The method of claim 11, wherein: the printcomprises a section view of the part for manufacture.
 19. The method ofclaim 18, wherein the at least a processor is configured to superimposethe first plurality of object lines of the computing model of the partfor manufacture onto the second plurality of object lines of the sectionview of the part for manufacture.
 20. The method of claim 19, whereinthe at least a processor is configured to transfer a second semanticdatum from the section view to the computer model.